Estimating vigilance level by using EEG and EMG signals
Estimating vigilance level by using EEG and EMG signals
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DOI：
10.1007/s00521-007-0117-7
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发表时间：
2008-06-01
影响因子：
6
通讯作者：
Bayram, Muhittin
中科院分区：
文献类型：
--
作者：
Akin, Mehmet;Kurt, Muhammed B.;Bayram, Muhittin
我们开发了一种新的方法来估计警觉水平，通过使用从清醒到睡眠过渡期间记录的EEG和EMG信号。以前的研究仅使用EEG信号来估计警觉水平。在这项研究中，它的目的是估计警觉水平，同时使用脑电和肌电信号，以提高估计率的准确性。在我们的工作中，EEG和EMG信号从30个主题。在数据准备阶段，利用小波变换将脑电信号分离到各个子带进行有效识别，并利用颏肌电信号进行运动伪迹的验证和消除。利用人工神经网络对清醒过渡到睡眠状态时的脑电和肌电变化进行诊断。训练和测试数据集包括子带分量的EEG和EMG信号的功率密度被施加到人工神经网络的训练和测试的系统，它给出了三种情况下的警觉水平的主题：清醒，困倦，和睡眠。估计的准确性约为98-99%，而先前仅使用EEG的研究的准确性为95- 96%。
We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98-99% while the accuracy of the previous study, which uses only EEG, was 95-96%.